Transfer entropy

To study the propagation pattern of congestion risk in the traffic network and enhance risk control capabilities, a model has been developed. This model takes into account the probabilities of five threats (the risk occurrence probability; the risk of loss; the unpredictability of risk; the uncontrollability of risk; the transferability of risk) in the traffic network to define the risk entropy and determine the risk capacity, analyze the mechanism of congestion risk propagation, and explore the impact of risk resistance, the average degree of risk capacity at intersections, and the degree of correlation on congestion risk propagation...

Isomerization of epoxides into versatile allylic alcohols is an atom-economical synthetic method to afford vicinal bifunctional groups. Comprehensive density functional theory (DFT) calculations were carried out to elucidate the complex mechanism of a bimetallic Ti/Co-catalyzed selective isomerization of epoxides to allyl alcohols by examining several possible pathways. Our results suggest a possible mechanism involving (1) radical-type epoxide ring opening catalyzed by Cp2 Ti(III)Cl leading to a Ti(IV)-bound β-alkyl radical, (2) hydrogen-atom transfer (HAT) catalyzed by the Co(II) catalyst to form the Ti(IV)-enolate and Co(III)-H intermediate, (3) protonation to give the alcohols, and (4) proton abstraction to form the Co(I) species followed by electron transfer to regenerate the active Co(II) and Ti(III) species...

The development of efficient and stable catalysts for hydrogen production from electrolytic water in a wide pH range is of great significance in alleviating the energy crisis. Herein, Pt nanoparticles (NPs) anchored on the vacancy of high entropy rare earth oxides (HEREOs) were prepared for the first time for highly efficient hydrogen production by water electrolysis. The prepared Pt-(LaCeSmYErGdYb)O showed excellent electrochemical performances, which require only 12, 57, and 77 mV to achieve a current density of 100 mA cm-2 in 0...

Nanoparticles composed of high-entropy alloys (HEA NPs) exhibit remarkable performance in electrocatalytic processes such as hydrogen evolution and oxidations. In this study, two types of quinary HEA NPs of PtRhPdIrRu, are synthesized, featuring disordered and crystallized nanostructures, both with and without a boiling mixture. The disordered HEA NPs (d-HEA NPs) with a size of 3.5 nm is synthesized under intense boiling conditions, attributed to improved heat and mass transfer during reduction of precursors and particle growth...

BACKGROUND: Anopheles stephensi is native to Southeast Asia and the Arabian Peninsula and has emerged as an effective and invasive malaria vector. Since invasion was reported in Djibouti in 2012, the global invasion range of An. stephensi has been expanding, and its high adaptability to the environment and the ongoing development of drug resistance have created new challenges for malaria control. Climate change is an important factor affecting the distribution and transfer of species, and understanding the distribution of An...

In this study, we present an enzyme-free fluorescence biosensor for the highly sensitive detection of miRNA-21, a crucial biomarker in clinical diagnosis. Our innovative approach combines catalytic hairpin assembly (CHA) and entropy-driven amplification into a cascade amplification strategy. MicroRNA initiates the catalytic hairpin assembly reaction, liberating the trigger region needed for the entropy-driven amplification reaction. This triggers a series of strand displacement reactions, resulting in the separation of the fluorescence resonance energy transfer pair and an amplified fluorescence signal from FAM...

The thermodynamic entropy of coarse-grained (CG) models stands as one of the most important properties for quantifying the missing information during the CG process and for establishing transferable (or extendible) CG interactions. However, performing additional CG simulations on top of model construction often leads to significant additional computational overhead. In this work, we propose a simple hierarchical framework for predicting the thermodynamic entropies of various molecular CG systems. Our approach employs a decomposition of the CG interactions, enabling the estimation of the CG partition function and thermodynamic properties a priori...

A logical sequence of information-theoretic quantifiers of directional (causal) couplings in Markov chains is generated within the framework of dynamical causal effects (DCEs), starting from the simplest DCEs (in terms of localization of their functional elements) and proceeding step-by-step to more complex ones. Thereby, a system of 11 quantifiers is readily obtained, some of them coinciding with previously known causality measures widely used in time series analysis and often called "information transfers" or "flows" (transfer entropy, Ay-Polani information flow, Liang-Kleeman information flow, information response, etc...

Emergent high entropy nanomaterials and their associated complex surface structure hold promise to unlock unique catalytic intermediate pathways and photonic/plasmonic interactions; however, synthetic strategies to tune the size, morphological, and stoichiometric properties remain limited. This work demonstrates a confined electro-precipitation mechanism for the formation of tunable, high-entropy oxide microspheres within emulsion droplet scaffolds. This mechanism complements a traditional confined electrodeposition mechanism and explains the previously observed anomalous formation of thermodynamically unfavorable particles, including lanthanide species...

The molecular weight of polymers can influence the material properties, but the molecular weight at the experiment level sometimes can be a huge burden for property prediction with full-atomic simulations. The traditional bottom-up coarse grain (CG) simulation can reduce the computation cost. However, the dynamic properties predicted by the CG simulation can deviate from the full-atomic simulation result. Usually, in CG simulations, the diffusion is faster and the viscosity and modulus are much lower. The fast dynamics in CG are usually solved by a posteriori scaling on time, temperature, or potential modifications, which usually have poor transferability to other non-fitted physical properties because of a lack of fundamental physics...

Sensory processing and sensorimotor integration are abnormal in dystonia, including impaired modulation of beta-corticomuscular coherence. However, cortex-muscle interactions in either direction are rarely described, with reports limited predominantly to investigation of linear coupling, using corticomuscular coherence or Granger causality. Information-theoretic tools such as transfer entropy detect both linear and non-linear interactions between processes. This observational case-control study applies transfer entropy to determine intra- and cross-frequency cortex-muscle coupling in young people with dystonia/dystonic cerebral palsy...

The use of fast in silico prediction methods for protein-ligand binding free energies holds significant promise for the initial phases of drug development. Numerous traditional physics-based models (e.g., implicit solvent models), however, tend to either neglect or heavily approximate entropic contributions to binding due to their computational complexity. Consequently, such methods often yield imprecise assessments of binding strength. Machine learning (ML) models provide accurate predictions and can often outperform physics-based models...

Green development of agriculture and rural areas (GDARA) is an essential part of rural revitalization and high-quality development. Based on 2011 to 2020 provincial panel data from China, the entropy-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model is constructed to measure the level of GDARA, finding that the overall level during the sample period was positive. Then the regional differences mainly from intra-regional were revealed by the methods of Dagum Gini coefficient and Kernel density estimation and were shrinking overall...

In the realm of healthcare, the demand for swift and precise diagnostic tools has been steadily increasing. This study delves into a comprehensive performance analysis of three pre-trained convolutional neural network (CNN) architectures: ResNet50, DenseNet121, and Inception-ResNet-v2. To ensure the broad applicability of our approach, we curated a large-scale dataset comprising a diverse collection of chest X-ray images, that included both positive and negative cases of COVID-19. The models' performance was evaluated using separate datasets for internal validation (from the same source as the training images) and external validation (from different sources)...

It is important to explore causal relationships in functional magnetic resonance imaging study. However, the traditional effective connectivity analysis method is easy to produce false causality, and the detection accuracy needs to be improved. In this paper, we introduce a novel functional magnetic resonance imaging effective connectivity method based on the asymmetry detection of transfer entropy, which quantifies the disparity in predictive information between forward and backward time, subsequently normalizing this disparity to establish a more precise criterion for detecting causal relationships while concurrently reducing computational complexity...

Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content. The emotional orientation of danmakus can reflect the attitudes and opinions of viewers on video segments, which can help video platforms optimize video content recommendation and evaluate users' abnormal emotion levels. Aiming at the problems of low transferability of traditional sentiment analysis methods in the danmaku domain, low accuracy of danmaku text segmentation, poor consistency of sentiment annotation, and insufficient semantic feature extraction, this paper proposes a video danmaku sentiment analysis method based on MIBE-RoBERTa-FF-BiLSTM...

The brain-computer interface (BCI) is a direct pathway of communication between the electrical activity of the brain and an external device. The present paper was aimed to investigate directed connectivity between different areas of the brain during motor imagery (MI)-based BCI. For this purpose, two methods were implemented including, Limited Penetrable Horizontal Visibility Graph (LPHVG) and Direct Lingam. The visibility graph (VG) is a robust algorithm for analyzing complex systems such as the brain. Direct Lingam uses a non-Gaussian model to extract causal links which is appropriate for analyzing large-scale connectivity...

Computer simulations of the temperature dependence of enzyme reactions using the empirical valence bond (EVB) method have proven to give very accurate results in terms of the thermodynamic activation parameters. Here, we analyze the reasons for why such simulations are able to correctly capture activation enthalpies and entropies and how sensitive these quantities are to parametrization of the reactive potential energy function. We examine first the solution reference reaction for the enzyme ketosteroid isomerase, which corresponds to the acetate catalyzed deprotonation of the steroid in water...

The catalytic dehydrogenation of substituted alkenones on noble metal catalysts supported on carbon (Pt/C, Pd/C, Rh/C, and Ru/C) was investigated in an organic phase under inert conditions. The dehydrogenation and semihydrogenation of the enone starting materials resulted in aromatic compounds (primary products), saturated cyclic ketones (secondary products), and cyclic alcohols (minor products). Pd/C exhibits the highest catalytic activity, followed by Pt/C and Rh/C. Aromatic compounds remain the primary products, even in the presence of hydrogen donors...

Magnesium batteries attract interest as alternative energy-storage devices because of elemental abundance and potential for high energy density. Development is limited by the absence of suitable cathodes, associated with poor diffusion kinetics resulting from strong interactions between Mg2+ and the host structure. V2PS10 is reported as a positive electrode material for rechargeable magnesium batteries. Cyclable capacity of 100 mAh g-1 is achieved with fast Mg2+ diffusion of 7.2[[EQUATION]]10-11-4[[EQUATION]]10-14 cm2s-1...